Multi-omics enrichment analysis of genomic cancer data

• Applicant: Dr. Ludwig Geistlinger
• Subject Area: Bioinformatics and Theoretical Biology ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Epidemiology and Medical Biometry/Statistics ; Human Genetics ; Public Health, Healthcare Research, Social and Occupational Medicine
• Term: from 2018 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 404178667

The microbiome is now known to influence the onset,progression, and treatment of cancers both within the gastrointestinal tract and systemically. Whole-metagenome shotgun sequencing (WMS) provides the highest resolution profiling of the human bacterial, archeal, and viral microbiome available, and enabled the discovery of Fusobacterium nucleatum as a risk factor and probable etiologic factor for colorectal cancer. Over 6,900 human WMS microbiome sequencing samples are already available in the Sequence Read Archive, including >1,100 from cancer patients. However, these data remain under-interpreted due to a lack of precise and integrative computational methods. Characterization and analysis of the human microbiome have been greatly catalyzed by advances in genomic technologies and analysis methods for gene expression data obtained from massive parallel DNA sequencing. Gene set enrichment analysis (GSEA) is an integral component of gene expression data analysis, allowing the discovery of coherent expression patterns among predefined gene signatures that share a biological function, property, or that were identified together by a previous study. All GSEA methods rely on comprehensive databases of signatures, equivalents of which do not yet exist for microbiota. For the extension of this project, we therefore aim to translate GSEA concepts and develop resources for its application to microbiome studies. This builds on systematically curated published signatures of differentially abundant microbes associated with cancer, antibiotics usage, experiments on animal models, randomized clinical trials, and microbial attributes. The development of statistically accurate microbe enrichment analysis in this project will allow researchers to better interpret the results of microbiome studies by comparing observed changes in the microbiome to previous results that are annotated for quality of evidence according to study design and sample size. Comparing findings across studies systematically improves researchers’ ability to prioritize causal factors in cancer etiology over those arising from confounding when proposing experimental follow-up or public health interventions.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Levi Waldron, Ph.D.